Statistical Mechanics of Learning

The effort to build machines that are able to learn and undertake tasks such as datamining, image processing and pattern recognition has led to the development of artificial neural networks in which learning from examples may be described and understood. The contribution to this subject made over the past decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics, and include many examples and exercises.

List of contents

1. Getting started; 2. Perceptron learning - basics; 3. A choice of learning rules; 4. Augmented statistical mechanics formulation; 5. Noisy teachers; 6. The storage problem; 7. Discontinuous learning; 8. Unsupervised learning; 9. On-line learning; 10. Making contact with statistics; 11. A bird's eye view: multifractals; 12. Multilayer networks; 13. On-line learning in multilayer networks; 14. What else?; Appendix A. Basic mathematics; Appendix B. The Gardner analysis; Appendix C. Convergence of the perceptron rule; Appendix D. Stability of the replica symmetric saddle point; Appendix E. 1-step replica symmetry breaking; Appendix F. The cavity approach; Appendix G. The VC-theorem.

Summary

Artificial neural networks provide a simple framework for describing learning from examples. This coherent account of important concepts and techniques of statistical mechanics and their application to learning theory comes with background material in mathematics and physics, plus many examples and exercises, making it ideal for courses, self-teaching, or reference.

Product details

• Authors: C. van den Broeck, A. Engel, Andreas Engel, Engel A., C. Van den Broeck, Christian P. Van Den Broeck
• Publisher: Cambridge Academic
• Languages: English
• Product format: Hardback
• Released: 31.03.2015
• EAN: 9780521773072
• ISBN: 978-0-521-77307-2
• Dimensions: 170 mm x 244 mm x 21 mm
• Weight: 750 g
• Illustrations: 1 table 136 exercises, Tabellen, nicht spezifiziert, Worked examples or Exercises
• Subjects: Guides; Natural sciences, medicine, IT, technology > IT, data processing > IT; Artificial Intelligence, COMPUTERS / Machine Theory, Artificial Intelligence (AI), Mathematical theory of computation, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning